[](https://pepy.tech/project/cspot)
[](https://github.com/nirmallab/cspot/actions/workflows/docs.yml)
[](https://github.com/nirmallab/cspot/actions/workflows/build-unix-mac-win.yml)
# 🐊 Getting Started with CSPOT
Kindly note that **CSPOT is not a plug-and-play solution**. It's a framework that requires significant upfront investment of time from potential users for training and validating deep learning models, which can then be utilized in a plug-and-play manner for processing large volumes of similar multiplexed imaging data.
## System Requirements:
**Hardware :**
`CSPOT` comprises two modules: training and prediction. Training can be efficiently executed on a standard laptop without the need for a GPU. However, for predictions, leveraging a GPU significantly enhances processing speed (particularly for large images).
**Software :**
This package is supported for Windows (10, 11), macOS (Sonoma, Ventura) and Linux (Ubuntu 16.04).
**Dependencies :**
The `pyproject.toml` file contains a comprehensive list of dependencies.
## Installation Guide:
**There are two ways to set it up based on how you would like to run the program**
- Using an interactive environment like Jupyter Notebooks
- Using Command Line Interface
Before we set up CSPOT, we highly recommend using a environment manager like Conda. Using an environment manager like Conda allows you to create and manage isolated environments with specific package versions and dependencies.
**Download and Install the right [conda](https://docs.conda.io/en/latest/miniconda.html) based on the opertating system that you are using**
## **Create a new conda environment**
```
# use the terminal (mac/linux) and anaconda promt (windows) to run the following command
conda create --name cspot -y python=3.9
conda activate cspot
```
**Install `cspot` within the conda environment.**
```
pip install cspot
```
The installation time for `cspot` generally falls under 5 minutes, based on internet speed and connectivity.
## **Interactive Mode**
Using IDE or Jupyter notebooks
```python
pip install notebook
# open the notebook and import CSPOT
import cspot as cs
# Go to the tutorial section to follow along
```
## **Command Line Interface**
```
wget https://github.com/nirmalLab/cspot/archive/main.zip
unzip main.zip
cd cspot-main/cspot
# Go to the tutorial section to follow along
```
## **Docker Container**
```
docker pull nirmallab/cspot:cspot
# Go to the tutorial section to follow along
```
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